Fuzzy Logic - Sorry

AI Module
Linguistic Hedges
APGDST
AI Module
Fuzzy inference system
APGDST
The contrast hedges change the
nature of the fuzzy region by
making it either less
fuzzy(intensification) or more
fuzzy(diffusion).
If µ is >= 0.5
1
µ A)
= (
2
1
(
2
µ A
( A))
1
µ A)
= 1−
(
2
1
(
2
µ A
( A))
A fuzzy inference system
essentially defines a nonlinear
mapping of the input data vector
into a scalar output using fuzzy
rules.
else if µ< 0.5
1
µ A)
= 1−
(
2
1
(
2
µ A
( A))
© NCST, 2002 Fuzzy Logic
21
© NCST, 2002 Fuzzy Logic
22
AI Module
Consider a multiinput and multioutput system. Let the input and
output vectors be represented as follows:
x =
T
x
Fuzzy inference system
T
x , x , x , x , x ) T
1 2 3 4
( 5
y = ( y , y , y , y , y ) T
x
1
2
T
k
x
3
T
1 1
T ( x)
= { , ,....... }
µ ( x ) = { , ,....... }
i
x
4
The linguistic variable x in the universe of discourse is characterized by
5
1 2
µ µ
x
x
i
µ
x
APGDST
AI Module
. The inputs are
Fuzzy inference system
x1=years of education
x2= years of experience
The output is
y=salary.
Let T(x1) = {low,medium,high} for U in range [0-15].
T(x2 )= { low,medium,high} for U in range [0-30].
Let T(y) = {very low, low, medium, high, very high}
for U in range [ 2000, 20000].
APGDST
© NCST, 2002 Fuzzy Logic
23
© NCST, 2002 Fuzzy Logic
24